



'''simple docstring'''



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCAmelCase_  :							Dict             =    datasets.utils.logging.get_logger(__name__)
@dataclass
class 						lowerCamelCase_				(       datasets.BuilderConfig      ):
						_lowerCAmelCase  : Optional[datasets.Features]						  =					None
						_lowerCAmelCase  : str						  =					"utf-8"
						_lowerCAmelCase  : Optional[str]						  =					None
						_lowerCAmelCase  : Optional[str]						  =					None
						_lowerCAmelCase  : bool						  =					True  # deprecated
						_lowerCAmelCase  : Optional[int]						  =					None  # deprecated
						_lowerCAmelCase  : int						  =					1_0 << 2_0  # 10MB
						_lowerCAmelCase  : Optional[bool]						  =					None


class 						lowerCamelCase_				(       datasets.ArrowBasedBuilder      ):
						_lowerCAmelCase  : List[str]						  =					JsonConfig







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													if self.config.block_size is not None:
																				logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''					)
																				SCREAMING_SNAKE_CASE       :   List[str]         =      self.config.block_size
													if self.config.use_threads is not True:
																				logger.warning(
																				    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''					)
													if self.config.newlines_in_values is not None:
																				raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''					)
													return datasets.DatasetInfo(features=self.config.features					)







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Tuple					):

													"""simple docstring"""
													if not self.config.data_files:
																				raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      dl_manager.download_and_extract(self.config.data_files					)
													if isinstance(lowerCAmelCase__					,     (str, list, tuple)					):
																				SCREAMING_SNAKE_CASE       :   Any         =      data_files
																				if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																											SCREAMING_SNAKE_CASE       :   List[str]         =      [files]
																				SCREAMING_SNAKE_CASE       :   str         =      [dl_manager.iter_files(lowerCAmelCase__					) for file in files]
																				return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,     gen_kwargs={'''files''': files}					)]
													SCREAMING_SNAKE_CASE       :   Dict         =      []
													for split_name, files in data_files.items():
																				if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																											SCREAMING_SNAKE_CASE       :   str         =      [files]
																				SCREAMING_SNAKE_CASE       :   Tuple         =      [dl_manager.iter_files(lowerCAmelCase__					) for file in files]
																				splits.append(datasets.SplitGenerator(name=lowerCAmelCase__					,     gen_kwargs={'''files''': files}					)					)
													return splits







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   pa.Table					):

													"""simple docstring"""
													if self.config.features is not None:
																				# adding missing columns
																				for column_name in set(self.config.features					) - set(pa_table.column_names					):
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.config.features.arrow_schema.field(lowerCAmelCase__					).type
																											SCREAMING_SNAKE_CASE       :   Any         =      pa_table.append_column(lowerCAmelCase__					,     pa.array([None] * len(lowerCAmelCase__					)					,     type=lowerCAmelCase__					)					)
																				# more expensive cast to support nested structures with keys in a different order
																				# allows str <-> int/float or str to Audio for example
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      table_cast(lowerCAmelCase__					,     self.config.features.arrow_schema					)
													return pa_table







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__					)					):
																				# If the file is one json object and if we need to look at the list of items in one specific field
																				if self.config.field is not None:
																											with open(lowerCAmelCase__					,     encoding=self.config.encoding					,     errors=self.config.encoding_errors					) as f:
																																		SCREAMING_SNAKE_CASE       :   Optional[Any]         =      json.load(lowerCAmelCase__					)

																											# We keep only the field we are interested in
																											SCREAMING_SNAKE_CASE       :   Dict         =      dataset[self.config.field]

																											# We accept two format: a list of dicts or a dict of lists
																											if isinstance(lowerCAmelCase__					,     (list, tuple)					):
																																		SCREAMING_SNAKE_CASE       :   Any         =      set().union(*[row.keys() for row in dataset]					)
																																		SCREAMING_SNAKE_CASE       :   List[Any]         =      {col: [row.get(lowerCAmelCase__					) for row in dataset] for col in keys}
																											else:
																																		SCREAMING_SNAKE_CASE       :   int         =      dataset
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      pa.Table.from_pydict(lowerCAmelCase__					)
																											yield file_idx, self._cast_table(lowerCAmelCase__					)

																				# If the file has one json object per line
																				else:
																											with open(lowerCAmelCase__					,     '''rb'''					) as f:
																																		SCREAMING_SNAKE_CASE       :   Tuple         =      0
																																		# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																																		# Set a default minimum value of 16kB if the chunk size is really small
																																		SCREAMING_SNAKE_CASE       :   Tuple         =      max(self.config.chunksize // 32					,     16 << 10					)
																																		SCREAMING_SNAKE_CASE       :   Dict         =      (
																																		    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																																		)
																																		while True:
																																									SCREAMING_SNAKE_CASE       :   str         =      f.read(self.config.chunksize					)
																																									if not batch:
																																																break
																																									# Finish current line
																																									try:
																																																batch += f.readline()
																																									except (AttributeError, io.UnsupportedOperation):
																																																batch += readline(lowerCAmelCase__					)
																																									# PyArrow only accepts utf-8 encoded bytes
																																									if self.config.encoding != "utf-8":
																																																SCREAMING_SNAKE_CASE       :   int         =      batch.decode(self.config.encoding					,     errors=lowerCAmelCase__					).encode('''utf-8'''					)
																																									try:
																																																while True:
																																																							try:
																																																														SCREAMING_SNAKE_CASE       :   List[Any]         =      paj.read_json(
																																																														    io.BytesIO(lowerCAmelCase__					)					,     read_options=paj.ReadOptions(block_size=lowerCAmelCase__					)					)
																																																														break
																																																							except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																														if (
																																																														    isinstance(lowerCAmelCase__					,     pa.ArrowInvalid					)
																																																														    and "straddling" not in str(lowerCAmelCase__					)
																																																														    or block_size > len(lowerCAmelCase__					)
																																																														):
																																																																					raise
																																																														else:
																																																																					# Increase the block size in case it was too small.
																																																																					# The block size will be reset for the next file.
																																																																					logger.debug(
																																																																					    F"""Batch of {len(lowerCAmelCase__					)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""					)
																																																																					block_size *= 2
																																									except pa.ArrowInvalid as e:
																																																try:
																																																							with open(
																																																							    lowerCAmelCase__					,     encoding=self.config.encoding					,     errors=self.config.encoding_errors					) as f:
																																																														SCREAMING_SNAKE_CASE       :   Tuple         =      json.load(lowerCAmelCase__					)
																																																except json.JSONDecodeError:
																																																							logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__					)}: {e}"""					)
																																																							raise e
																																																# If possible, parse the file as a list of json objects and exit the loop
																																																if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):  # list is the only sequence type supported in JSON
																																																							try:
																																																														SCREAMING_SNAKE_CASE       :   Any         =      set().union(*[row.keys() for row in dataset]					)
																																																														SCREAMING_SNAKE_CASE       :   int         =      {col: [row.get(lowerCAmelCase__					) for row in dataset] for col in keys}
																																																														SCREAMING_SNAKE_CASE       :   Dict         =      pa.Table.from_pydict(lowerCAmelCase__					)
																																																							except (pa.ArrowInvalid, AttributeError) as e:
																																																														logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__					)}: {e}"""					)
																																																														raise ValueError(F"""Not able to read records in the JSON file at {file}."""					) from None
																																																							yield file_idx, self._cast_table(lowerCAmelCase__					)
																																																							break
																																																else:
																																																							logger.error(F"""Failed to read file '{file}' with error {type(lowerCAmelCase__					)}: {e}"""					)
																																																							raise ValueError(
																																																							    F"""Not able to read records in the JSON file at {file}. """
																																																							    F"""You should probably indicate the field of the JSON file containing your records. """
																																																							    F"""This JSON file contain the following fields: {str(list(dataset.keys()					)					)}. """
																																																							    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """					) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																									yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__					)
																																									batch_idx += 1









'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




